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<p>This article focus on designing and programming an application for implementing the YOLO method v8 in the detection and subsequent classification of objects in video recordings. The application, developed in the Python programming language, allows the insertion of video recordings in mp4 format. It then divides the frames, enabling the use of the YOLO method for object detection, classification, and simultaneous determination of the time and frame at which the identified object is located. Compared to previous versions, YOLOv8 from 2023 allows the use of up to 53 convolutional layers. As part of the YOLO method implementation, therefore was devised a convolutional network with 5 layers and 5 object classes. Throughout the training process, was configured a total of 10 epochs, resulting in an accuracy of 94.79%. Notably, after the 7th epoch, the error rate exhibited a declining trend, reaching a value of 0.15. These values signify a sufficiently trained network without the need for further retraining.</p>

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Comparison classification algorithms and the YOLO method for video analysis and object detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月14日

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  • Martin Magdin,
  • Zoltán Balogh

摘要

This article focus on designing and programming an application for implementing the YOLO method v8 in the detection and subsequent classification of objects in video recordings. The application, developed in the Python programming language, allows the insertion of video recordings in mp4 format. It then divides the frames, enabling the use of the YOLO method for object detection, classification, and simultaneous determination of the time and frame at which the identified object is located. Compared to previous versions, YOLOv8 from 2023 allows the use of up to 53 convolutional layers. As part of the YOLO method implementation, therefore was devised a convolutional network with 5 layers and 5 object classes. Throughout the training process, was configured a total of 10 epochs, resulting in an accuracy of 94.79%. Notably, after the 7th epoch, the error rate exhibited a declining trend, reaching a value of 0.15. These values signify a sufficiently trained network without the need for further retraining.